reverse-skills-max: initial public release (restored, redacted, hardened)

vendor-new/analyst-ai-pack/skills/writing-yara-rules-from-reversed-code/SKILL.md

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+---
+name: writing-yara-rules-from-reversed-code
+description: 'Turns reverse-engineering findings into durable YARA detections: selecting stable
+  code constructs and constants over volatile strings, extracting opcode/byte patterns with
+  wildcards, and validating rules for low false positives. Activates for requests to write a
+  YARA rule from reversed code, create a detection signature, or convert RE findings into
+  hunting rules.'
+domain: cybersecurity
+subdomain: reverse-engineering
+tags:
+  - reverse-engineering
+  - yara
+  - detection
+  - signatures
+  - threat-hunting
+version: 1.0.0
+author: analyst-ai-pack
+license: Apache-2.0
+mitre_attack:
+  - T1027.001
+  - T1587.001
+d3fend:
+  - D3-FCR
+  - D3-FA
+references:
+  - 'YARA documentation — https://yara.readthedocs.io/'
+  - 'YARA writing-better-rules guidance — https://github.com/Neo23x0/YARA-Style-Guide'
+---
+
+# Writing YARA Rules from Reversed Code
+
+## When to Use
+
+- You finished reversing a sample/family and want a detection that survives recompilation and
+  cosmetic changes.
+- You need byte-pattern signatures from decryptors, API-hash constants, or unique algorithms
+  rather than fragile strings.
+- You are converting RE notes into hunting/scanning rules for a corpus.
+
+**Do not use** volatile artifacts (file paths, mutex names that change per build, packer stubs
+shared across unrelated families) as your primary anchor — they cause drift and false hits.
+
+## Prerequisites
+
+- The `yara` engine (and ideally `yara-python`) for testing.
+- A small corpus: target samples (true positives) and clean/unrelated files (false-positive
+  control).
+
+## Workflow
+
+### Step 1: Choose stable anchors
+
+Prefer, in order: a unique algorithm's opcode sequence (decryptor, hashing loop), embedded
+magic constants (API hashes, XOR keys, S-box), then distinctive strings only if intrinsic.
+
+### Step 2: Extract byte patterns with wildcards
+
+Pull the relevant opcodes and wildcard volatile operands (addresses, immediates) so the rule
+survives relocation/recompilation:
+
+```text
+$dec = { 8A 04 ?? 34 ?? 88 04 ?? 41 3B ?? 7? ?? }   ; xor-decrypt loop, regs/disp wildcarded
+```
+
+### Step 3: Assemble the rule
+
+Combine 2–3 independent anchors with a `condition` requiring enough of them, plus a cheap
+prefilter (file size, PE magic) to keep scanning fast:
+
+```bash
+python scripts/analyst.py scaffold --name family_xyz --hash 0xABCDEF12
+```
+
+### Step 4: Validate against the corpus
+
+Run the rule across true positives and the clean control set; require all TPs match and zero
+FPs on the control.
+
+```bash
+yara -r rules/family_xyz.yar ./corpus
+```
+
+### Step 5: Tune and document
+
+Adjust thresholds, add `meta` (author, date, reference, hash), and record which construct each
+string anchors so future analysts can maintain it.
+
+## Validation
+
+- Rule matches all intended samples and produces zero hits on the clean control set.
+- Anchors map to intrinsic code/constants, not build-specific noise.
+- `meta` documents source samples and the reasoning for each pattern.
+
+## Pitfalls
+
+- Wildcarding too little (brittle) or too much (false positives); wildcard only volatile bytes.
+- Anchoring on shared library/packer code present in unrelated binaries.
+- Skipping the clean-corpus test, shipping a noisy rule.
+
+## References
+
+- See [`references/api-reference.md`](references/api-reference.md) for the rule scaffolder.
+- YARA documentation and style guide (linked in frontmatter).